
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



				__A     ,				__A       =					image.size
				__A     ,				__A       =					(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
				__A       =					image.resize((w, h)						,  resample=PIL_INTERPOLATION["lanczos"]		)
				__A       =					np.array(a_		).astype(np.floataa		) / 255.0
				__A       =					image[None].transpose(0						,  3						,  1						,  2		)
				__A       =					torch.from_numpy(a_		)
				return 2.0 * image - 1.0





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def __init__(			self   :					Tuple				,A   :					VQModel				,A   :					UNetaDModel				,A   :					Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ]				,):
					super().__init__()
					self.register_modules(vqvae=A				,unet=A				,scheduler=A					)







	@torch.no_grad()
	def __call__(			self   :					Optional[Any]				,A   :					Union[torch.Tensor, PIL.Image.Image] = None				,A   :					Optional[int] = 1				,A   :					Optional[int] = 1_00				,A   :					Optional[float] = 0.0				,A   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None				,A   :					Optional[str] = "pil"				,A   :					bool = True				,):
					if isinstance(A				,PIL.Image.Image					):
									__A       =					1
					elif isinstance(A				,torch.Tensor					):
									__A       =					image.shape[0]
					else:
									raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A					)}'''					)

					if isinstance(A				,PIL.Image.Image					):
									__A       =					preprocess(A					)

					__A     ,				__A       =					image.shape[-2:]

					# in_channels should be 6: 3 for latents, 3 for low resolution image
					__A       =					(batch_size, self.unet.config.in_channels // 2, height, width)
					__A       =					next(self.unet.parameters()					).dtype

					__A       =					randn_tensor(A				,generator=A				,device=self.device				,dtype=A					)

					__A       =					image.to(device=self.device				,dtype=A					)

					# set timesteps and move to the correct device
					self.scheduler.set_timesteps(A				,device=self.device					)
					__A       =					self.scheduler.timesteps

					# scale the initial noise by the standard deviation required by the scheduler
					__A       =					latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__A       =					"eta" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
					__A       =					{}
					if accepts_eta:
									__A       =					eta

					for t in self.progress_bar(A					):
									# concat latents and low resolution image in the channel dimension.
									__A       =					torch.cat([latents, image]				,dim=1					)
									__A       =					self.scheduler.scale_model_input(A				,A					)
									# predict the noise residual
									__A       =					self.unet(A				,A					).sample
									# compute the previous noisy sample x_t -> x_t-1
									__A       =					self.scheduler.step(A				,A				,A				,**A					).prev_sample

					# decode the image latents with the VQVAE
					__A       =					self.vqvae.decode(A					).sample
					__A       =					torch.clamp(A				,-1.0				,1.0					)
					__A       =					image / 2 + 0.5
					__A       =					image.cpu().permute(0				,2				,3				,1					).numpy()

					if output_type == "pil":
									__A       =					self.numpy_to_pil(A					)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A					)


"""simple docstring"""



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a       :						List[str]       		=		logging.get_logger(__name__)




class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = question_encoder
        _UpperCAmelCase        = generator
        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  )		->Dict:






        '''simple docstring'''






        if os.path.isfile(__UpperCamelCase  ):
            raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""  )
        os.makedirs(__UpperCamelCase  ,       exist_ok=__UpperCamelCase  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """question_encoder_tokenizer"""  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """generator_tokenizer"""  )
        self.question_encoder.save_pretrained(__UpperCamelCase  )
        self.generator.save_pretrained(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       **__UpperCamelCase      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        from ..auto.tokenization_auto import AutoTokenizer

        _UpperCAmelCase        = kwargs.pop("""config"""  ,       __UpperCamelCase  )

        if config is None:
            _UpperCAmelCase        = RagConfig.from_pretrained(__UpperCamelCase  )

        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.question_encoder  ,       subfolder="""question_encoder_tokenizer"""  )
        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.generator  ,       subfolder="""generator_tokenizer"""  )
        return cls(question_encoder=__UpperCamelCase  ,       generator=__UpperCamelCase  )







    def __call__(      self      :							Any  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Optional[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return self.current_tokenizer(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Tuple  )		->Any:






        '''simple docstring'''






        return self.generator.batch_decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  ,       *__UpperCamelCase      :							Optional[int]  ,       **__UpperCamelCase      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        return self.generator.decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.generator







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							str = "longest"  ,       __UpperCamelCase      :							str = None  ,       __UpperCamelCase      :							bool = True  ,       **__UpperCamelCase      :							Tuple  ,       )		->BatchEncoding:






        '''simple docstring'''






        warnings.warn(
            """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
            """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
            """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
            """details"""  ,       __UpperCamelCase  ,       )
        if max_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            __UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            text_target=__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        _UpperCAmelCase        = labels["""input_ids"""]
        return model_inputs